#!/usr/bin/env python
# coding: utf-8

# In[1]:


from keras.preprocessing.image import load_img, img_to_array
from pathlib import Path


# In[2]:


current_dir = Path.cwd()
target_image_path = str(current_dir) + '\Desktop\TF\Manjaro.jpg'
style_reference_image_path = str(current_dir) + '\Desktop\TF\Starry-Night-vr-Background.jpg'


# In[3]:


print(current_dir)


# In[4]:


width, height = load_img(target_image_path).size
img_height = 400
img_width = int(width * img_height / height)


# In[5]:


import numpy as np
from keras.applications import vgg19


# In[20]:


def preprocess_image(image_path):
    img = load_img(image_path, target_size=(img_height, img_width))
    img = img_to_array(img)
    img = np.expand_dims(img, axis=0)
    img = vgg19.preprocess_input(img)
    return img

def deprocess_image(x):
    x[:,:,0] += 103.939
    x[:,:,1] += 116.779
    x[:,:,2] += 123.68 
    x = x[:,:, ::-1]
    x = np.clip(x,0,255).astype('uint8')
    return x


# In[7]:


#from keras import backend as K
from tensorflow.keras import backend as K
import tensorflow as tf
tf.compat.v1.disable_eager_execution()


# In[8]:


target_image = K.constant(preprocess_image(target_image_path))
style_reference_image = K.constant(preprocess_image(style_reference_image_path))
combination_image = K.placeholder((1, img_height, img_width, 3))
input_tensor = K.concatenate([target_image, style_reference_image, combination_image], axis=0)

model = vgg19.VGG19(input_tensor=input_tensor, weights='imagenet', include_top=False)
print('Model loaded.')


# In[9]:


def content_loss(base, combination):
    return K.sum(K.square(combination - base))


# In[10]:


def gram_matrix(x):
    features = K.batch_flatten(K.permute_dimensions(x, (2,0,1)))
    gram = K.dot(features, K.transpose(features))
    return gram

def style_loss(style, combination):
    S = gram_matrix(style)
    C = gram_matrix(combination)
    channels = 3
    size = img_height * img_width
    return K.sum(K.square(S - C)) / (4. * (channels ** 2) * (size ** 2))


# In[11]:


def total_variation_loss(x):
    a = K.square(x[:,:img_height - 1, :img_width - 1, :] - x[:, 1:, :img_width - 1, :])
    b = K.square(x[:,:img_height - 1, :img_width - 1, :] - x[:, :img_height - 1, 1:, :])
    return K.sum(K.pow(a + b, 1.25))


# In[12]:


outputs_dict = dict([(layer.name, layer.output) for layer in model.layers])
content_layer = 'block5_conv2'
style_layers = ['block1_conv1',
               'block2_conv1',
               'block3_conv1',
               'block4_conv1',
               'block5_conv1']
total_variation_weight = 1e-4
style_weight = 1.
content_weight = 0.025

loss = K.variable(0.)
layer_features = outputs_dict[content_layer]
target_image_features = layer_features[0, :, :, :]
combination_features = layer_features[2, :, :, :]
loss = loss + content_weight * content_loss(target_image_features, combination_features)

for layer_name in style_layers:
    layer_features = outputs_dict[layer_name]
    style_reference_features = layer_features[1, :, :, :]
    combination_features = layer_features[2, :, :, :]
    sl = style_loss(style_reference_features, combination_features)
    loss += (style_weight / len(style_layers)) * sl
    
loss += total_variation_weight * total_variation_loss(combination_image)


# In[15]:


grads = K.gradients(loss, combination_image)[0]

fetch_loss_and_grads = K.function([combination_image], [loss, grads])

class Evaluator(object):
    
    def __init__(self):
        self.loss_value = None
        self.grad_values = None
        
    def loss(self, x):
        assert self.loss_value is None
        x = x.reshape((1, img_height, img_width, 3))
        outs = fetch_loss_and_grads([x])
        loss_value = outs[0]
        grad_values = outs[1].flatten().astype('float64')
        self.loss_value = loss_value
        self.grad_values = grad_values
        return self.loss_value
    
    def grads(self, x):
        assert self.loss_value is not None
        grad_values = np.copy(self.grad_values)
        self.loss_value = None
        self.grad_values = None
        return grad_values
    
evaluator = Evaluator()


# In[18]:


from scipy.optimize import fmin_l_bfgs_b
#from scipy.misc import imsave
from keras.preprocessing.image import save_img as imsave
import time


# In[ ]:


result_prefix = 'my_result'
iterations = 20

x = preprocess_image(target_image_path)
x.flatten()
for i in range(iterations):
    print('Start of iteration ', i)
    start_time = time.time()
    x, min_val, info = fmin_l_bfgs_b(evaluator.loss, x, fprime=evaluator.grads, maxfun=20)
    print('Current loss value: ', min_val)
    img = x.copy().reshape((img_height, img_width, 3))
    img = deprocess_image(img)
    fname = result_prefix + '_at_iteration_%d.png' %i
    imsave(fname, img)
    print('Image saved as ', fname)
    end_time = time.time()
    print('Iteration %d completed in %ds ' % (i, end_time - start_time))


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